=Paper=
{{Paper
|id=Vol-1766/oaei16_paper11
|storemode=property
|title=LYAM++ results for OAEI 2016
|pdfUrl=https://ceur-ws.org/Vol-1766/oaei16_paper11.pdf
|volume=Vol-1766
|authors=Abdel Nasser Tigrine,Zohra Bellahsene,Konstantin Todorov
|dblpUrl=https://dblp.org/rec/conf/semweb/TigrineBT16
}}
==LYAM++ results for OAEI 2016==
https://ceur-ws.org/Vol-1766/oaei16_paper11.pdf
LYAM++ Results for OAEI 2016
Abdel Nasser Tigrine, Zohra Bellahsene, Konstantin Todorov
{lastname}@lirmm.fr
LIRMM / University of Montpellier, France
Abstract. LYAM++ is a fully automatic ontology matching system
based on the use of external sources. Our approach applies a novel or-
chestration of the components of the matching workflow. We present our
results on anatomy, conference large biomedical and Multifarm tracks of
OAEI2016.
1 Presentation of the System
In spite of the considerable advance that has been made in the field of on-
tology matching recently, many questions remain open [1]. The current work
addresses the challenge of using background knowledge with a focus on aligning
cross-lingual ontologies, i.e., ontologies defined in different natural languages [2].
Indeed, considering multilingual and cross-lingual information is becoming
more and more important, in view particularly of the growing number of web
content-creating non-English users and the clear demand of cross-language in-
teroperability. In the context of the web of data, it is important to propose
procedures for linking vocabularies across natural languages, in order to foster
the creation of a veritable global information network.
The use of different natural languages in the concepts and relations labeling
process is becoming an important source of ontology heterogeneity. The methods
that have been proposed to deal with it most commonly rely on automatic
translation of labels to a single target language [3,4] or apply machine learning
techniques [2]. However, machine translation tolerates low precision levels and
machine learning methods require large training corpus that is rarely available
in an ontology matching scenario. An inherent problem of translation is that
there is often a lack of exact one-to-one correspondence between the terms in
different natural languages.
1.1 State, Purpose, General Statement
We present LYAM++ (Yet Another Matcher - Light)[5], a fully automatic
ontology matching system based on the use of external sources. LYAM++ does
not rely on machine translation for cross-lingual ontology matching. Instead, we
make use of the openly available general-purpose multilingual semantic network
BabelNet1 in order to recreate the missing semantic context in the matching
1
http://babelnet.org/
2 N. Tigrine, Z. Bellahsene, K. Todorov
Fig. 1: The processing pipeline of LYAM++.
process. Another original feature of our approach is the choice of orchestration of
the matching workflow. The novel workflow orchestration provides better results
compared to the classical one. We refer the reader to the results reported in [5].
1.2 Specific Techniques Used
The workflow of LYAM++ is given in Fig 1. The overall process consists of
four main components: a terminological matcher, a mapping selection module
and, finally, a structural matcher. One of the original contributions of this work
is the choice of orchestration of these components. Indeed, the places of the
mapping selection module and the structural matcher are reversed in the exist-
ing OM tools [6]. However, we wanted to ensure that we feed only good quality
mappings to the structural matcher, therefore we decided to filter the discov-
ered correspondences right after producing the initial alignment. This decision
is supported experimentally in[5].
The terminological matching module, the second contribution described in
this paper, acts on the one hand as a preprocessing component and, on the
other hand – as a light-weight terminological matcher between labels. We start
by splitting the elements of each ontology in three groups: labels of classes, labels
of object properties and labels of data object properties (in colors blue, black
and red in the figure), since these groups of elements are to be aligned separately.
A standard preprocessing procedure is applied on these sets of labels, comprising
character normalization, stop-words filtering, tokenization and lemmatization.
For the cross-lingual ontology matching, at first every token of a given label
s in in the source ontology S is enriched by related terms and synonyms from
BabelNet and all of these terms are represented in the language lT (language of
the target ontology), which makes these terms comparable to the tokens of the
labels in the target ontology T . A simple similarity evaluation by the help of the
Jaccard coefficient selects the term in each set of related terms corresponding to
a given token from s that has the highest score with respect to every token in
each label of T . This helps to restitute the label s in the language lT . Finally,
Cross-lingual Ontology Matching 3
the labels in each group of S and T , seen as sets of tokens, are compared by
using the Soft TFIDF similarity measure [7], which produces an intermediate
terminological alignment. For monolingual ontology matching, the system uses
the relations such as ”hasSynonyms” present in a given BK to match between
two concepts.
The three remaining components are standard OM modules [6], although
ordered in a new manner. The Mapping selection is a module that transforms the
initial 1 to many mapping to a 1:1 mapping based on the principle of iteratively
retaining the pairs of concepts with maximal value of similarity. Finally, the
structural matcher component filters the trustworthy pairs of aligned concepts
by looking at the similarity values produced for their parents and their children
in the ontology hierarchies.
1.3 Adaptations made for the evaluation
The adaptation made for the evaluation is in the preprocessing step. LYAM++
uses (1) Uberon [8] for anatomy and BioMed tracks, (2) BabelNet [9] for confer-
ence and multifarm tracks.
1.4 Links to the System and to the Set of Provided Alignments
Last year, the system was not available online because it depends heavily on
the use of BabelNet 3.0 version, which is under a non-free licence. In this year,
we used old version of BabelNet 2.0 which is under free license.
The alignments produced by LYAM++ for this year’s can be found under the
following link: http://www.lirmm.fr/benellefi/Alignements.rar. LYAM++
can be found under the following link: http://www.lirmm.fr/benellefi/Lyam+
+.rar
2 Results
We have evaluated our approach on data coming from the ontology align-
ment evaluation initiative (OAEI)2 and particularly anatomy, conference, large
biomedical and multifarm.
Anatomy This track aims to discovering alignments between a human anatomy
ontology, part of the NCI Thesaurus3 and a mouse anatomy ontology. This track
is considered as a large-scale matching task because the input ontologies are of
a large size and very rich semantically. Table 1 presents the results obtained by
LYAM++ on this year’s
Conference This track contains 16 ontologies from the scientific publication
field. Table 2 presents the results obtained by LYAM++ on this year’s
2
http://oaei.ontologymatching.org/
3
https://ncit.nci.nih.gov/ncitbrowser/
4 N. Tigrine, Z. Bellahsene, K. Todorov
Table 1: Results of LYAM++ for anatomy .
F-M Recall Precision
LYAM++ 0.87 0.88 0.86
Table 2: Results of LYAM++ for conference .
F-M Recall Precision
ra1-M1 0.36 0.18 0.48
ra1-M2 0.34 0.57 0.13
ra1-M3 0.29 0.15 0.38
ra2-M1 0.36 0.19 0.52
ra2-M2 0.35 0.59 0.13
ra2-M3 0.31 0.16 0.41
Large biomedical ontologies This track aims at aligning three large biomedi-
cal ontologies, namely FMA, SNOMED and the NCI Thesaurus. Table 3 presents
the results obtained by LYAM++ on this year’s
Table 3: Results of LYAM++ for BioMed.
F-M Recall Precision
Small FMA-NCI 0.79 0.88 0.72
MultiFarm is a benchmark designed for evaluating cross-lingual ontology match-
ing systems. Multifarm data consist of a set of 7 ontologies originally coming from
the Conference benchmark of OAEI, translated into 8 languages. Two evaluation
tasks are defined: task 1 consists in matching two different ontologies given in
different languages, while task 2 aims to align different language versions of one
single ontology.
Table 4 presents the results obtained by LYAM++ on this year’s Multi-
farm evaluation campaign. What we see is the average F-measure value for all
language-pairs without any threshold on the confidence measure. The value in
the parenthesis corresponds to the average F-measure value for the generated
alignments only (the pairs of languages that the system handles).
3 Conclusion
In this paper, we present the over view of the LYAM++ system and our
results on the OAEI2016 tracks . In this year, our goal was to participate on
monolingual ontology matching scenarios. We used Babelnet 2.0 version instead
of Babelnet 3.0 version due to the licenses problems. Subjects of ongoing and
Cross-lingual Ontology Matching 5
Table 4: Results of LYAM++ for Multifarm.
Task1 Task2
LYAM++ 0.01 0.02
future work are (1) testing and evaluating different sources of external knowledge,
(2) applying semantic mappings selection methods to improve the results, (3)
adaptation of the approach to the large scale ontology matching scenarios.
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